scRNA-seq analysis using Seurat package
Long Nguyen
Description:
- The purpose of this notebook is to analyze scRNA-seq data of mouse Estrus
- The method used is reading directly from mtx files (CellRanger count outputs)
- The algorithms used come from Seurat package
- Two mitochondiral genes: mt-Nd6 and mt-Cytb will be manually deleted
- Because read_loom method in Scanpy does not contain those genes after filtering was done
- The results will compared to:
- Same dataset analyzed using Scanpy - ManuallyRemoveGenes
- Same dataset analyzed using Scanpy - readloom
Workflow begin
This tutorial comes from Seurat
Load required packages
library(dplyr)
library(Seurat)
library(leiden)
library(ggplot2)
library(patchwork)Load data
Input is a directory that contains CellRanger count outputs
previous_time <- proc.time()[3]
Estrus.data <-Read10X(data.dir ="/home/nguyen/Estrus_cellranger_count/Estrus_Raw/")
proc.time()[3] - previous_timeelapsed
7.678 - View(Estrus.data) - run this command to investigate the output
- Current dataset:
- 31253 genes
- 6599 cells
Initialize the Seurat object
- Initialize the Seurat object with the raw (non-normalized data).
- Filter out genes that are detected in less than 3 cells
- Filter out cells that have less than 200 genes expressed
previous_time <- proc.time()[3]
Estrus <- CreateSeuratObject(counts = Estrus.data, project = "Estrus", min.cells = 3, min.features = 200)
proc.time()[3] - previous_timeelapsed
1.49 EstrusAn object of class Seurat
19420 features across 6481 samples within 1 assay
Active assay: RNA (19420 features, 0 variable features)- Output:
- 19420 genes
- 6481 cells
Mitochondiral genes overview
# This is the list of genes that start with mt
mito_genes <- grep("^mt-", rownames(Estrus) , ignore.case=T, value=T)
print(mito_genes) [1] "mt-Nd1" "mt-Nd2" "mt-Co1" "mt-Co2" "mt-Atp8" "mt-Atp6" "mt-Co3" "mt-Nd3" "mt-Nd4l" "mt-Nd4" "mt-Nd5" "mt-Nd6" "mt-Cytb"mt-Nd6 and mt-Cytb will be manually deleted
counts <- GetAssayData(Estrus, assay = "RNA")
counts <- counts[-(which(rownames(counts) %in% c("mt-Nd6", "mt-Cytb"))),]
Estrus <- subset(Estrus, features = rownames(counts))# This is the list of genes that start with mt
mito_genes <- grep("^mt-", rownames(Estrus) , ignore.case=T, value=T)
print(mito_genes) [1] "mt-Nd1" "mt-Nd2" "mt-Co1" "mt-Co2" "mt-Atp8" "mt-Atp6" "mt-Co3" "mt-Nd3" "mt-Nd4l" "mt-Nd4" "mt-Nd5" - “mt-Nd6”, “mt-Cytb” were deleted
Calculate the percentage of mitochondrial genes
- Add “percent.mt” column to object metadata
- Percentage of mitochondrial genes is stored in “percent.mt”
- “^mt-” means strings start with mt (lowercase mt for mice, uppercase MT for human)
Estrus[["percent.mt"]] <- PercentageFeatureSet(Estrus, pattern = "^mt-")# This is the data in "percent.mt" column
percent_mito <- FetchData(object = Estrus, vars = "percent.mt") #how to fetch data
head(percent_mito)# Visualize QC metrics as a violin plot
VlnPlot(Estrus, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Filter out low-quality cells
- We filter out cells that have unique feature counts over 6000
- Because any cell that has more than 6000 genes are likely doublets
- Note: Seurat tutorial used 2500 instead of 6000
- We also filter out cells that have more than 5% mitochondrial genes
- Because those are likely to be nonviable cells
previous_time <- proc.time()[3]
Estrus <- subset(Estrus, subset = nFeature_RNA < 6000 & percent.mt < 5)
proc.time()[3] - previous_timeelapsed
0.327 EstrusAn object of class Seurat
19418 features across 2585 samples within 1 assay
Active assay: RNA (19418 features, 5000 variable features)
1 dimensional reduction calculated: pca- After filtering step, the dataset is left with
- 19418 genes
- 2585 cells
Normalizing the data
- Perform log-normalization method
- Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor
- scale.factor default value = 10,000
- This is then natural-log transformed using log1p
previous_time <- proc.time()[3]
Estrus <- NormalizeData(Estrus, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|proc.time()[3] - previous_timeelapsed
0.261 Identification of highly variable features (feature selection)
previous_time <- proc.time()[3]
Estrus <- FindVariableFeatures(Estrus, selection.method = "disp", nfeatures = 5000)Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|proc.time()[3] - previous_timeelapsed
0.493 Plotting highly variable features
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(Estrus), 10)
top10 [1] "Hbb-bs" "Hba-a1" "Hba-a2" "Hbb-bt" "Dcpp3" "Ccl21a" "Apoe" "Cd74" "Ccl5" "Dcpp1" # Plot variable features with and without labels
plot1 <- VariableFeaturePlot(Estrus)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2 Warning: Removed 1 rows containing missing values (geom_point).
Warning: Removed 1 rows containing missing values (geom_point).
Regress out data and perform scaling
- Regressing out total counts per cell and percent.mt before scaling.
- Take out mitochondrial genes before scaling (analyzing) the data
- Because mito_genes shouldn’t be accounted for cell population analysis.
previous_time <- proc.time()[3]
all.genes <- rownames(Estrus)
Estrus <- ScaleData(Estrus, features = all.genes, vars.to.regress = c("nCount_RNA","percent.mt"), scale.max = 10)Regressing out nCount_RNA, percent.mt
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Centering and scaling data matrix
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|============================================================================================================================| 100%# Explanations below are from [Seurat](https://satijalab.org/seurat/archive/v3.0/cell_cycle_vignette.html)
# vars.to.regress = RegressOut in Seurat version 1.4 or lower
# However, as the results of this procedure are stored in the scaled data slot (therefore overwriting the output of ScaleData)
# This functionality is merged into the ScaleData function itself.
proc.time()[3] - previous_timeelapsed
724.018 Perform linear dimensional reduction (PCA)
previous_time <- proc.time()[3]
Estrus <- RunPCA(Estrus, features = VariableFeatures(object = Estrus))PC_ 1
Positive: Rps28, Rps19, Rpl18a, Rpl13, Rpl23a, Rplp0, Rpl37, Rpl32, Rps12, Rps18
Rps5, Rps24, Rps29, Rpl37a, Rps7, Rps27a, Rps23, Rps11, Rpl35, Rps4x
Rpl39, Cd63, Rps20, Rpl27a, Rps15a, Rps16, Rpl10, Rps3a1, Rpl19, Rps9
Negative: Gm867, 1700016K19Rik, Ccdc153, 1700007K13Rik, Tmem212, Dynlrb2, Gm19935, 1110017D15Rik, Cfap126, Fam166c
Fam183b, Sntn, Vpreb3, Foxj1, Rsph1, Tctex1d4, Dnali1, Lrrc10b, Pifo, 1700024G13Rik
Prr29, Dnah12, Hdc, Mlf1, 2410004P03Rik, Tppp3, Efcab10, Timp4, Dnah5, Elof1
PC_ 2
Positive: Lcn2, Cd68, Fxyd3, Arg1, Sftpd, Wfdc2, Sprr2f, Pdzk1ip1, Ltf, Prap1
Cxcl17, Serpina1e, Clca1, Padi1, Pigr, Rnf128, Muc1, Tmem45b, Padi2, Muc4
Ly6a, Tacstd2, Epcam, Ckmt1, Fxyd4, Elf3, Trpv6, Pglyrp1, Car2, Fut9
Negative: Serpinh1, Sparc, Col1a2, Col3a1, Cald1, Col1a1, Col6a1, Bgn, Mgp, Igfbp7
Col6a2, Rarres2, Aebp1, Zbtb20, Dcn, Nbl1, Rcn3, Igfbp6, Mdk, Sparcl1
Csrp2, Fxyd1, Mfap2, Fxyd6, Col6a3, Lgals1, Col4a1, Igf1, Pbx1, Gsn
PC_ 3
Positive: Ovgp1, Gstm2, Wfdc18, Emb, Kctd14, Plet1, Aldoc, Krt18, Tpd52l1, Clu
Agr2, Cldn22, Gstm1, Tmem218, Cbs, Ppp1r1b, Hddc2, Slc1a3, Rcn1, Lrpap1
Spint2, Shisa8, Dkk3, Rbp1, Kcne3, Manf, Tmed3, Dcxr, Mgst1, Cystm1
Negative: Ctss, Tyrobp, Fcer1g, Csf1r, Laptm5, Apoe, Cd52, H2-Ab1, Cd74, H2-Aa
H2-Eb1, Coro1a, C1qc, Aif1, C1qa, C1qb, Lyz2, H2-DMa, Cx3cr1, Pld4
Ms4a6c, Cybb, Ly86, Ms4a7, Spi1, Mpeg1, B2m, Trf, Fyb, Fcgr3
PC_ 4
Positive: Clca1, Cxcl17, Muc4, Prap1, Pigr, Serpina1e, Slc40a1, Plac8, Ly6a, Sprr2f
Tmem45b, Trpv6, Arg1, Tacstd2, Tmprss4, Fxyd4, Prxl2a, Padi2, Tspan1, Fut9
Fcgbp, Ckmt1, Car2, Lrg1, Morrbid, Pdzk1ip1, Wfdc2, Upk3bl, Sftpd, Ncoa7
Negative: Ctsc, Tyrobp, Fcer1g, Cd52, H2-Aa, C1qc, Cd74, Csf1r, Aif1, C1qa
C1qb, H2-Eb1, Lyz2, Coro1a, H2-Ab1, Cx3cr1, Pld4, Ly86, Ms4a6c, Cybb
Ms4a7, Spi1, Cyth4, Fcgr3, H2-DMa, Lcp1, Mpeg1, Laptm5, Lst1, Adgre1
PC_ 5
Positive: Gsn, Pdgfra, Htra3, Mfap4, Mfap2, Dpt, Serpinf1, Sparcl1, Ntrk2, Col6a3
Col15a1, Penk, Gpm6b, Lama2, Lpl, Matn2, Ramp3, Mdk, Spon1, Ramp1
Rxfp1, Col5a2, Islr, Osr2, Srpx, Itih5, Zeb2, Htra1, Bgn, Enpp2
Negative: Cavin2, Gpm6a, Kdr, Cav1, Egfl7, Podxl, Ecscr, Adgrf5, Cldn5, Pecam1
Esam, Cdh5, Prss23, Arhgap29, Upk3b, Mmrn1, Tie1, Flt4, Lrrn4, Upk1b
Nkain4, Emcn, Stmn2, Ccl21a, C2, Ltbp4, Ushbp1, Mmrn2, Myrf, Lmcd1 proc.time()[3] - previous_timeelapsed
4.545 Examine and visualize PCA results a few different ways
print(Estrus[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: Rps28, Rps19, Rpl18a, Rpl13, Rpl23a
Negative: Gm867, 1700016K19Rik, Ccdc153, 1700007K13Rik, Tmem212
PC_ 2
Positive: Lcn2, Cd68, Fxyd3, Arg1, Sftpd
Negative: Serpinh1, Sparc, Col1a2, Col3a1, Cald1
PC_ 3
Positive: Ovgp1, Gstm2, Wfdc18, Emb, Kctd14
Negative: Ctss, Tyrobp, Fcer1g, Csf1r, Laptm5
PC_ 4
Positive: Clca1, Cxcl17, Muc4, Prap1, Pigr
Negative: Ctsc, Tyrobp, Fcer1g, Cd52, H2-Aa
PC_ 5
Positive: Gsn, Pdgfra, Htra3, Mfap4, Mfap2
Negative: Cavin2, Gpm6a, Kdr, Cav1, Egfl7 VizDimLoadings(Estrus, dims = 1:2, reduction = "pca")
DimPlot(Estrus, reduction = "pca") + ggtitle(label = 'Including immune cells')
ggsave(filename = "PCA_Including_immune_cells.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 7.29 x 4.5 in image
FeaturePlot(Estrus, reduction = "pca", features = c('Ovgp1',
'Foxj1', 'Igf1', 'Pdxk',
'Fxyd4', 'Bsg',
'Serpina1e', 'Krt19'))
ggsave(filename = "PCA_Including_immune_cells1.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(Estrus, reduction = "pca", features = c('Dcn', 'Pax8',
'Ephx2',
'Pecam1', 'Pdgfra', 'Myh11', 'Atp2b4',
'Cd52'))
ggsave(filename = "PCA_Including_immune_cells2.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageDetermine the ‘dimensionality’ of the data
- Seurat tutorial uses JackStraw() and and ScoreJackStraw to determine dimensionality
- the syntax is:
- Estrus <- JackStraw(Estrus, num.replicate = 100)
- Estrus <- ScoreJackStraw(Estrus, dims = 1:20)
- However, it takes very long.
- An elbow plot often corresponds well enough with the significant dims and is much faster to run than Jackstraw
- According to the graph below, choose the first 22 principle components to use
ElbowPlot(Estrus, ndims = 40)
Find neighbors
- Dims = dimensions = determined as 22 from the above step
previous_time <- proc.time()[3]
Estrus_new <- FindNeighbors(Estrus, dims = 1:22, k.param = 10)Computing nearest neighbor graph
Computing SNNproc.time()[3] - previous_timeelapsed
0.324 Find cell clusters using Leiden’s method
# Algorithm = 4 specifies Leiden's method
previous_time <- proc.time()[3]
Estrus_new <- FindClusters(Estrus_new, resolution = 0.14, algorithm = 4)
proc.time()[3] - previous_timeelapsed
0.754 Constructing UMAP plot
previous_time <- proc.time()[3]
Estrus_new <- RunUMAP(Estrus_new, dim = 1:22) 16:23:56 UMAP embedding parameters a = 0.9922 b = 1.112
16:23:56 Read 2585 rows and found 22 numeric columns
16:23:56 Using Annoy for neighbor search, n_neighbors = 30
16:23:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:23:57 Writing NN index file to temp file /tmp/RtmprVQ6Uz/file19849734bf80
16:23:57 Searching Annoy index using 1 thread, search_k = 3000
16:23:57 Annoy recall = 100%
16:23:57 Commencing smooth kNN distance calibration using 1 thread
16:23:58 Initializing from normalized Laplacian + noise
16:23:58 Commencing optimization for 500 epochs, with 102266 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:24:00 Optimization finishedproc.time()[3] - previous_timeelapsed
3.653 Visualization
DimPlot(Estrus_new, reduction = "umap") + ggtitle(label = 'Including immune cells')
ggsave(filename = "UMAP_Including_immune_cells.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 7.29 x 4.5 in image
FeaturePlot(Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Dcn', 'Serpina1e', 'Sprr2f',
'Fxyd4', 'Ly6a', 'Epcam', 'Krt19', 'Vim', 'Pdgfra'))
ggsave(filename = "UMAP_Including_immune_cells1.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(Estrus_new, reduction = "umap", features = c('Myh11',
'Dkk2', 'Slitrk6', 'Dio2', 'Smoc2', 'Tes', 'Dab1', 'Camk1d', 'Pecam1', 'Cd52','Mki67'))
ggsave(filename = "UMAP_Including_immune_cells2.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageRemoving immune cells
sub_Estrus = subset(x = Estrus_new, ident = c(1,2,3,4,5,6,7,9,10,12))
DimPlot(sub_Estrus, reduction = "umap") + ggtitle(label = 'Immune cells removed')
ggsave(filename = "UMAP_Immune_cells_removed.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(sub_Estrus, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Fxyd4', 'Cxcl17', 'Myh11', 'Cd52', 'Twist2', 'Pecam1'))
ggsave(filename = "UMAP_Immune_cells_removed1.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(sub_Estrus, reduction = "umap", features = c('Pdgfra', 'Ephx2', 'Dcn', 'Krt19', 'Epcam', 'Vim','Mki67'))
ggsave(filename = "UMAP_Immune_cells_removed2.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imagesub_EstrusAn object of class Seurat
19418 features across 2318 samples within 1 assay
Active assay: RNA (19418 features, 5000 variable features)
2 dimensional reductions calculated: pca, umap- After removing immune cells, the dataset is left with:
- 19418 genes
- 2318 cells
Re-run analysis after excluding immune cells
previous_time <- proc.time()[3]
sub_Estrus <- RunPCA(sub_Estrus, features = VariableFeatures(object = sub_Estrus))PC_ 1
Positive: Gm867, Ccdc153, 1700016K19Rik, 1700007K13Rik, Tmem212, Fam166c, Dynlrb2, Gm19935, Sntn, 1110017D15Rik
Fam183b, Cfap126, Foxj1, Vpreb3, Dnali1, Rsph1, Tctex1d4, Lrrc10b, 1700024G13Rik, Dnah12
Pifo, Prr29, 2410004P03Rik, Mlf1, Hdc, Tppp3, Dnah5, Efcab10, Timp4, Dnaja4
Negative: Rps28, Rps19, Rplp0, Rpl18a, Rpl13, Rpl23a, Rpl37, Rpl32, Rps18, Rps5
Rps7, Rps12, Rpl37a, Rps24, Rps27a, Cd63, Rpl39, Rps23, Rps4x, Rps29
Rps20, Rpl35, Rpl27a, Rps15a, Rps11, Rps3a1, Rpl10a, Rpl10, Rps2, Rps16
PC_ 2
Positive: Cald1, Col1a2, Serpinh1, Col3a1, Col6a1, Col1a1, Aebp1, Bgn, Rarres2, Mgp
Sparc, Col6a2, Dcn, Igfbp7, Igfbp6, Rcn3, Rnase4, Zbtb20, Mdk, Lgals1
Nbl1, Csrp2, Fxyd1, Igf1, Sparcl1, Lrp1, Mfap2, Col6a3, Timp2, Ccdc80
Negative: Lcn2, Fxyd3, Ltf, Epcam, Sftpd, Wfdc2, Plet1, Arg1, Pdzk1ip1, Rnf128
Sprr2f, Muc1, Padi1, Prap1, Cxcl17, Cldn7, Elf3, Serpina1e, Clca1, Ier3
Pigr, Ly6a, Ckmt1, Tmem45b, Car2, Muc4, Bcat1, Tspan8, Pglyrp1, Tacstd2
PC_ 3
Positive: Ovgp1, Kctd14, Gstm2, Emb, Clu, Tmem218, Gstm1, Cbs, Wfdc18, Ppp1r1b
Slc1a3, Aldoc, Agr2, Cldn22, Dkk3, Rcn1, Lrpap1, Shisa8, Fn1, Tpd52l1
Sdf2l1, Smim14, Selenom, Manf, Gdpd2, Bex4, Enho, Tmed3, Kcne3, Slco2a1
Negative: Plac8, Clca1, Muc4, Pigr, Cxcl17, Ly6a, Slc40a1, Prap1, Trpv6, Tmem45b
Serpina1e, Sprr2f, Tspan1, Tmprss4, Tacstd2, Padi2, Prxl2a, Fcgbp, Cfh, Arg1
Fxyd4, Fut9, Lrg1, Rasa4, Fbln1, Morrbid, Slc39a8, Ckmt1, Ncoa7, Upk3bl
PC_ 4
Positive: Upk3b, Nkain4, Lrrn4, Upk1b, C2, Myrf, Tmem151a, Lgals7, Fgf1, Gpm6a
Lgals2, Smoc2, Serpinb6b, Slpi, Dpp4, Cldn15, Rspo1, Msln, Plxna4, Cybrd1
Aldh1a2, Nrgn, Fmod, Rpp25, Ogn, Hpgd, Prph, Adam33, Ptgis, Hacd4
Negative: Sparcl1, Pdgfra, Gsn, Mfap4, Htra3, Dpt, Mfap2, Col15a1, Itih5, Gpm6b
Ramp3, Lama2, Ntrk2, Penk, G0s2, Serpinf1, Spon1, Col6a3, Rxfp1, Plpp3
Lpl, Enpp2, Lhfp, Matn2, Ramp1, Nrp1, Atp1a2, Srpx, Emilin1, Pdgfrl
PC_ 5
Positive: Pdgfra, Dcn, Dpt, Col15a1, Ramp3, Ntrk2, Lpl, Htra3, Htra1, Plxdc2
Aebp1, Cygb, Col5a3, Meg3, Lum, P2ry1, Fbln1, Dkk2, Pcolce, Dio2
Lrp1, Mfap5, Ctsk, Spon1, Col5a2, Col3a1, Ctsl, Matn2, Itih5, Rnase4
Negative: Myh11, Lmod1, Cnn1, Fbxl22, Acta2, Mustn1, Tagln, Actg2, Mcam, Sh3bgr
Des, Nexn, Myl9, Asb2, Ppp1r14a, Mylk, Tpm2, Synpo2, Myom1, Carmn
Slc22a3, Aoc3, Pdlim3, Slc2a4, Postn, Npnt, Tpm1, Dmpk, Smtn, Rbpms2 proc.time()[3] - previous_timeelapsed
4.742 DimPlot(sub_Estrus, reduction = "pca") + ggtitle(label = 'Immune cells removed')
ggsave(filename = "PCA_Immune_cells_removed.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(sub_Estrus, reduction = "pca", features = c('Esr1', 'Ovgp1', 'Foxj1', 'Fxyd4', 'Ephx2', 'Dcn'))
ggsave(filename = "PCA_Immune_cells_removed1.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(sub_Estrus, reduction = "pca", features = c('Fos', 'Kcne3', 'Serpina1e', 'Crabp2', 'Pgr', 'Vim'))
ggsave(filename = "PCA_Immune_cells_removed2.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageprevious_time <- proc.time()[3]
sub_Estrus_new <- FindNeighbors(sub_Estrus, dims = 1:22)Computing nearest neighbor graph
Computing SNNproc.time()[3] - previous_timeelapsed
0.375 previous_time <- proc.time()[3]
sub_Estrus_new <- FindClusters(sub_Estrus_new, resolution = 0.04, algorithm = 4)
proc.time()[3] - previous_timeelapsed
0.711 previous_time <- proc.time()[3]
sub_Estrus_new <- RunUMAP(sub_Estrus_new, dim = 1:22)16:42:15 UMAP embedding parameters a = 0.9922 b = 1.112
16:42:15 Read 2318 rows and found 22 numeric columns
16:42:15 Using Annoy for neighbor search, n_neighbors = 30
16:42:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:42:15 Writing NN index file to temp file /tmp/RtmprVQ6Uz/file1984921d941dd
16:42:15 Searching Annoy index using 1 thread, search_k = 3000
16:42:16 Annoy recall = 100%
16:42:16 Commencing smooth kNN distance calibration using 1 thread
16:42:16 Initializing from normalized Laplacian + noise
16:42:16 Commencing optimization for 500 epochs, with 91758 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:42:18 Optimization finishedproc.time()[3] - previous_timeelapsed
3.395 DimPlot(sub_Estrus_new, reduction = "umap") + ggtitle(label = 'Rescale UMAP after removing Immune cells')
ggsave(filename = "UMAP_Rescale.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 10 x 6.18 in imageFeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Crabp2', 'Myh11', 'Wt1', 'S100g', 'Sprr2f',
'Fxyd4'))
ggsave(filename = "UMAP_Rescale1.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Dcn', 'Pax8', 'Pax2', 'Twist2', 'Igf1', 'Serpina1e', 'Pdgfra', 'Krt19', 'Mki67'))
ggsave(filename = "UMAP_Rescale2.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageMarker genes
marker_genes = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4', 'Igfbp6',
'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1')UMAP for marker genes
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4'))
ggsave(filename = "UMAP_for_Marker_genes1.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageFeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Igfbp6',
'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1'))
ggsave(filename = "UMAP_for_Marker_genes2.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageRename clusters
Cluster.names <- c('1 Ciliated_InfAmp',
'2 Secretory_InfAmp',
'3 Epithelial_subset',
'4 Fibroblast Pdgfra+',
'5 Secretory_IsthUTJ',
'6 Fibroblast Pdgfra-',
'7 Muscle')
names(Cluster.names) <- levels(sub_Estrus_new)
sub_Estrus_new <- RenameIdents(sub_Estrus_new, Cluster.names)
DimPlot(sub_Estrus_new, reduction = "umap", pt.size = 0.5) + scale_color_manual(labels = Cluster.names,values=c('green', 'red','blue','orange','grey','yellow','pink','purple'))
ggsave(filename = "UMAP_Clusters_names.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 7.29 x 4.5 in image
DotPlot(sub_Estrus_new, features = marker_genes, group.by = sub_Estrus_new@meta.data$ident) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_y_discrete(labels = Cluster.names)
ggsave(filename = "DotPlot_for_Marker_genes.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 12 x 7.41 in imageNumber of cells in each clusters
table(sub_Estrus_new@active.ident, sub_Estrus_new@meta.data$orig.ident)
Estrus
1 Ciliated_InfAmp 538
2 Secretory_InfAmp 499
3 Epithelial_subset 351
4 Fibroblast Pdgfra+ 347
5 Secretory_IsthUTJ 304
6 Fibroblast Pdgfra- 152
7 Muscle 127Extract top genes for each cluster
- Default method of finding highly expressed genes for FindAllMarkers is Wilcoxon Rank Sum test
previous_time <- proc.time()[3]
sub_Estrus_new_markers <- FindAllMarkers(sub_Estrus_new, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 1 Ciliated_InfAmp
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 2 % ~13s
|++ | 3 % ~13s
|+++ | 4 % ~13s
|+++ | 5 % ~12s
|++++ | 6 % ~12s
|++++ | 7 % ~12s
|+++++ | 8 % ~12s
|+++++ | 9 % ~12s
|++++++ | 10% ~12s
|++++++ | 11% ~12s
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|+++++++ | 13% ~11s
|++++++++ | 14% ~11s
|++++++++ | 15% ~11s
|+++++++++ | 16% ~11s
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|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~10s
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|++++++++++++++++ | 31% ~09s
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|++++++++++++++++++ | 34% ~09s
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|+++++++++++++++++++ | 37% ~08s
|++++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|+++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|+++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s Calculating cluster 2 Secretory_InfAmp
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
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|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 3 Epithelial_subset
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 8 % ~03s
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|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
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|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
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|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
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|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
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|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~01s
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|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
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|++++++++++++++++++++++++++++++++ | 62% ~01s
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|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
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|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
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|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
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|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 4 Fibroblast Pdgfra+
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
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|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
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|+++++++++++++ | 25% ~02s
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|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s sub_Estrus_new_markers %>%
group_by(cluster) %>%
slice_max(n = 20, order_by = avg_log2FC)proc.time()[3] - previous_timeelapsed
38.09 Heat map for top 10 genes in each cluster
sub_Estrus_new_markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(sub_Estrus_new, features = top10$gene) + NoLegend()
ggsave(filename = "HeatMap_Top10_genes.pdf", plot = last_plot(), device = "pdf",
path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")Saving 10 x 6.18 in imagesaveRDS(sub_Estrus_new, file = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Estrus_subset_analyzed.rds")---
title: "scRNA-seq analysis using Seurat package"
output: html_notebook
author: Long Nguyen
---
# Description:
* The purpose of this notebook is to analyze scRNA-seq data of mouse Estrus
* The method used is reading directly from mtx files (CellRanger count outputs)
* The algorithms used come from Seurat package
* Two mitochondiral genes: mt-Nd6 and mt-Cytb will be manually deleted
* Because read_loom method in Scanpy does not contain those genes after filtering was done
* The results will compared to:
  + Same dataset analyzed using Scanpy - ManuallyRemoveGenes
  + Same dataset analyzed using Scanpy - readloom
  
# Workflow begin
This tutorial comes from [Seurat](https://satijalab.org/seurat/articles/pbmc3k_tutorial.html)

## Load required packages
```{r}
library(dplyr)
library(Seurat)
library(leiden)
library(ggplot2)
library(patchwork)
```


## Load data
Input is a directory that contains CellRanger count outputs
```{r}
previous_time <- proc.time()[3]
Estrus.data <-Read10X(data.dir ="/home/nguyen/Estrus_cellranger_count/Estrus_Raw/")
proc.time()[3] - previous_time
```
* View(Estrus.data) - run this command to investigate the output
* Current dataset:
  + 31253 genes
  + 6599 cells

## Initialize the Seurat object
* Initialize the Seurat object with the raw (non-normalized data).
* Filter out genes that are detected in less than 3 cells 
* Filter out cells that have less than 200 genes expressed
```{r}
previous_time <- proc.time()[3]
Estrus <- CreateSeuratObject(counts = Estrus.data, project = "Estrus", min.cells = 3, min.features = 200)
proc.time()[3] - previous_time
Estrus
```
* Output:
  + 19420 genes 
  + 6481 cells

## Mitochondiral genes overview
```{r}
# This is the list of genes that start with mt
mito_genes <- grep("^mt-", rownames(Estrus) , ignore.case=T, value=T)
print(mito_genes)
```

## mt-Nd6 and mt-Cytb will be manually deleted
```{r}
counts <- GetAssayData(Estrus, assay = "RNA")
counts <- counts[-(which(rownames(counts) %in% c("mt-Nd6", "mt-Cytb"))),]
Estrus <- subset(Estrus, features = rownames(counts))
```

```{r}
# This is the list of genes that start with mt
mito_genes <- grep("^mt-", rownames(Estrus) , ignore.case=T, value=T)
print(mito_genes)
```
* "mt-Nd6", "mt-Cytb" were deleted

## Calculate the percentage of mitochondrial genes
* Add "percent.mt" column to object metadata
* Percentage of mitochondrial genes is stored in "percent.mt"
* "^mt-" means strings start with mt (lowercase mt for mice, uppercase MT for human)
```{r}
Estrus[["percent.mt"]] <- PercentageFeatureSet(Estrus, pattern = "^mt-")
```

```{r}
# This is the data in "percent.mt" column
percent_mito <- FetchData(object = Estrus, vars = "percent.mt") #how to fetch data
head(percent_mito)
```

```{r, fig.width=10}
# Visualize QC metrics as a violin plot
VlnPlot(Estrus, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
```
```{r}
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
```

## Filter out low-quality cells
* We filter out cells that have unique feature counts over 6000
* Because any cell that has more than 6000 genes are likely doublets
* Note: Seurat tutorial used 2500 instead of 6000
* We also filter out cells that have more than 5% mitochondrial genes
* Because those are likely to be nonviable cells
```{r}
previous_time <- proc.time()[3]
Estrus <- subset(Estrus, subset = nFeature_RNA < 6000 & percent.mt < 5)
proc.time()[3] - previous_time
```
```{r}
Estrus
```
* After filtering step, the dataset is left with
 + 19418 genes
 + 2585 cells

## Normalizing the data
* Perform log-normalization method
* Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor
* scale.factor default value = 10,000
* This is then natural-log transformed using log1p
```{r}
previous_time <- proc.time()[3]
Estrus <- NormalizeData(Estrus, normalization.method = "LogNormalize", scale.factor = 10000)
proc.time()[3] - previous_time
```
## Identification of highly variable features (feature selection)
```{r}
previous_time <- proc.time()[3]
Estrus <- FindVariableFeatures(Estrus, selection.method = "disp", nfeatures = 5000)
proc.time()[3] - previous_time
```
## Plotting highly variable features
```{r}
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(Estrus), 10)
top10
```

```{r, fig.width=10, fig.height=4}
# Plot variable features with and without labels
plot1 <- VariableFeaturePlot(Estrus)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2 
```
## Regress out data and perform scaling
* Regressing out total counts per cell and percent.mt before scaling. 
* Take out mitochondrial genes before scaling (analyzing) the data 
* Because mito_genes shouldn't be accounted for cell population analysis.
```{r}
previous_time <- proc.time()[3]
all.genes <- rownames(Estrus)
Estrus <- ScaleData(Estrus, features = all.genes, vars.to.regress = c("nCount_RNA","percent.mt"), scale.max = 10)
# Explanations below are from [Seurat](https://satijalab.org/seurat/archive/v3.0/cell_cycle_vignette.html)
# vars.to.regress = RegressOut in Seurat version 1.4 or lower
# However, as the results of this procedure are stored in the scaled data slot (therefore overwriting the output of ScaleData) 
# This functionality is merged into the ScaleData function itself.
proc.time()[3] - previous_time
```
## Perform linear dimensional reduction (PCA)
```{r}
previous_time <- proc.time()[3]
Estrus <- RunPCA(Estrus, features = VariableFeatures(object = Estrus))
proc.time()[3] - previous_time
```

## Examine and visualize PCA results a few different ways
```{r}
print(Estrus[["pca"]], dims = 1:5, nfeatures = 5)
```

```{r}
VizDimLoadings(Estrus, dims = 1:2, reduction = "pca")
```

```{r}
DimPlot(Estrus, reduction = "pca") + ggtitle(label = 'Including immune cells') 
ggsave(filename = "PCA_Including_immune_cells.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

```{r, fig.width=12}
FeaturePlot(Estrus, reduction = "pca", features = c('Ovgp1', 
                                                    'Foxj1', 'Igf1', 'Pdxk', 
                                                    'Fxyd4', 'Bsg', 
                                                    'Serpina1e', 'Krt19'))
ggsave(filename = "PCA_Including_immune_cells1.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r, fig.width=12}
FeaturePlot(Estrus, reduction = "pca", features = c('Dcn', 'Pax8',
                                                    'Ephx2', 
                                                    'Pecam1', 'Pdgfra', 'Myh11', 'Atp2b4', 
                                                    'Cd52'))
ggsave(filename = "PCA_Including_immune_cells2.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

## Determine the ‘dimensionality’ of the data
* Seurat tutorial uses JackStraw() and and ScoreJackStraw to determine dimensionality
* the syntax is: 
  + Estrus <- JackStraw(Estrus, num.replicate = 100)
  + Estrus <- ScoreJackStraw(Estrus, dims = 1:20)
* However, it takes very long. 
* An elbow plot often corresponds well enough with the significant dims and is much faster to run than Jackstraw
* According to the graph below, choose the first 22 principle components to use
```{r}
ElbowPlot(Estrus, ndims = 40)
```
## Find neighbors
* Dims = dimensions = determined as 22 from the above step
```{r}
previous_time <- proc.time()[3]
Estrus_new <- FindNeighbors(Estrus, dims = 1:22, k.param = 10)
proc.time()[3] - previous_time
```
## Find cell clusters using Leiden's method
```{r}
# Algorithm = 4 specifies Leiden's method
previous_time <- proc.time()[3]
Estrus_new <- FindClusters(Estrus_new, resolution = 0.14, algorithm = 4)
proc.time()[3] - previous_time
```
## Constructing UMAP plot
```{r}
previous_time <- proc.time()[3]
Estrus_new <- RunUMAP(Estrus_new, dim = 1:22) 
proc.time()[3] - previous_time
```
## Visualization
```{r}
DimPlot(Estrus_new, reduction = "umap") + ggtitle(label = 'Including immune cells')
ggsave(filename = "UMAP_Including_immune_cells.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r, fig.width=12}
FeaturePlot(Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Dcn', 'Serpina1e', 'Sprr2f',
                                                         'Fxyd4', 'Ly6a', 'Epcam', 'Krt19', 'Vim', 'Pdgfra'))
ggsave(filename = "UMAP_Including_immune_cells1.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

```{r, fig.width=12}
FeaturePlot(Estrus_new, reduction = "umap", features = c('Myh11', 
                                        'Dkk2', 'Slitrk6', 'Dio2', 'Smoc2', 'Tes', 'Dab1', 'Camk1d', 'Pecam1', 'Cd52','Mki67'))
ggsave(filename = "UMAP_Including_immune_cells2.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
## Removing immune cells
```{r, fig.width=12}
sub_Estrus = subset(x = Estrus_new, ident = c(1,2,3,4,5,6,7,9,10,12))
DimPlot(sub_Estrus, reduction = "umap") + ggtitle(label = 'Immune cells removed')
ggsave(filename = "UMAP_Immune_cells_removed.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Fxyd4', 'Cxcl17', 'Myh11', 'Cd52', 'Twist2', 'Pecam1'))
ggsave(filename = "UMAP_Immune_cells_removed1.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "umap", features = c('Pdgfra', 'Ephx2', 'Dcn', 'Krt19', 'Epcam', 'Vim','Mki67'))
ggsave(filename = "UMAP_Immune_cells_removed2.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r}
sub_Estrus
```
* After removing immune cells, the dataset is left with:
  + 19418 genes
  + 2318 cells

## Re-run analysis after excluding immune cells
```{r}
previous_time <- proc.time()[3]
sub_Estrus <- RunPCA(sub_Estrus, features = VariableFeatures(object = sub_Estrus))
proc.time()[3] - previous_time
```

```{r, fig.width=12}
DimPlot(sub_Estrus, reduction = "pca") + ggtitle(label = 'Immune cells removed')
ggsave(filename = "PCA_Immune_cells_removed.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "pca", features = c('Esr1', 'Ovgp1', 'Foxj1', 'Fxyd4', 'Ephx2', 'Dcn'))
ggsave(filename = "PCA_Immune_cells_removed1.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "pca", features = c('Fos', 'Kcne3', 'Serpina1e', 'Crabp2', 'Pgr', 'Vim'))
ggsave(filename = "PCA_Immune_cells_removed2.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r}
previous_time <- proc.time()[3]
sub_Estrus_new <- FindNeighbors(sub_Estrus, dims = 1:22)
proc.time()[3] - previous_time
```

```{r}
previous_time <- proc.time()[3]
sub_Estrus_new <- FindClusters(sub_Estrus_new, resolution = 0.04, algorithm = 4)
proc.time()[3] - previous_time
```

```{r}
previous_time <- proc.time()[3]
sub_Estrus_new <- RunUMAP(sub_Estrus_new, dim = 1:22)
proc.time()[3] - previous_time
```

```{r, fig.width=10}
DimPlot(sub_Estrus_new, reduction = "umap") + ggtitle(label = 'Rescale UMAP after removing Immune cells')
ggsave(filename = "UMAP_Rescale.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Crabp2', 'Myh11', 'Wt1', 'S100g', 'Sprr2f',
                  'Fxyd4'))
ggsave(filename = "UMAP_Rescale1.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Dcn', 'Pax8', 'Pax2', 'Twist2', 'Igf1', 'Serpina1e', 'Pdgfra', 'Krt19', 'Mki67'))
ggsave(filename = "UMAP_Rescale2.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
## Marker genes
```{r}
marker_genes = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4', 'Igfbp6',
                 'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1')
```

## UMAP for marker genes
```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4'))
ggsave(filename = "UMAP_for_Marker_genes1.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Igfbp6',
                                                             'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1'))
ggsave(filename = "UMAP_for_Marker_genes2.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```

## Rename clusters
```{r}
Cluster.names <- c('1 Ciliated_InfAmp',
                   '2 Secretory_InfAmp',
                   '3 Epithelial_subset',
                   '4 Fibroblast Pdgfra+',
                   '5 Secretory_IsthUTJ',
                   '6 Fibroblast Pdgfra-',
                   '7 Muscle')
names(Cluster.names) <- levels(sub_Estrus_new)
sub_Estrus_new <- RenameIdents(sub_Estrus_new, Cluster.names)
DimPlot(sub_Estrus_new, reduction = "umap", pt.size = 0.5) + scale_color_manual(labels = Cluster.names,values=c('green',                                                                                        'red','blue','orange','grey','yellow','pink','purple'))
ggsave(filename = "UMAP_Clusters_names.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r, fig.width=12}
DotPlot(sub_Estrus_new, features = marker_genes, group.by = sub_Estrus_new@meta.data$ident) +  
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  scale_y_discrete(labels = Cluster.names)
ggsave(filename = "DotPlot_for_Marker_genes.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
## Number of cells in each clusters
```{r}
table(sub_Estrus_new@active.ident, sub_Estrus_new@meta.data$orig.ident)
```

## Extract top genes for each cluster
* Default method of finding highly expressed genes for FindAllMarkers is Wilcoxon Rank Sum test
```{r}
previous_time <- proc.time()[3]
sub_Estrus_new_markers <- FindAllMarkers(sub_Estrus_new, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
sub_Estrus_new_markers %>%
    group_by(cluster) %>%
    slice_max(n = 20, order_by = avg_log2FC)
proc.time()[3] - previous_time
```
## Heat map for top 10 genes in each cluster
```{r, fig.width=10}
sub_Estrus_new_markers %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(sub_Estrus_new, features = top10$gene) + NoLegend()
ggsave(filename = "HeatMap_Top10_genes.pdf", plot = last_plot(), device = "pdf",
         path = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Figures/")
```
```{r}
saveRDS(sub_Estrus_new, file = "/home/nguyen/FinalWork/R/ManuallyRemoveGenes/Estrus_subset_analyzed.rds")
```

